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Record W4224555021 · doi:10.1053/j.gastro.2022.04.030

Prediction Models for Celiac Disease Development in Children From High-Risk Families: Data From the PreventCD Cohort

2022· article· en· W4224555021 on OpenAlexfundno aff
C. Meijer, Renata Auricchio, Hein Putter, Gemma Castillejo, Paula Crespo, Judit Gyimesi, Corina Hartman, Sanja Kolaček, Sibylle Koletzko, Ilma R. Korponay‐Szabó, Eva Martinez Ojinaga, Isabel Polanco, Carmen Ribes‐Koninckx, Raanan Shamir, Hania Szajewska, Riccardo Troncone, Vincenzo Villanacci, Katharina Werkstetter, M. Luisa Mearin

Bibliographic record

VenueGastroenterology · 2022
Typearticle
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsnot available
FundersKomitet Badań NaukowychInstituto de Salud Carlos IIIAzrieli FoundationHungarian Scientific Research FundEuropean Commission
KeywordsMedicineCohortProportional hazards modelCumulative incidenceIncidence (geometry)Regression analysisDiseaseInternal medicineCohort studyProspective cohort studyStatistics

Abstract

fetched live from OpenAlex

BACKGROUND & AIMS: Screening for celiac disease (CD) is recommended in children with affected first-degree relatives (FDR). However, the frequency of screening and at what age remain unknown. The aims of this study were to detect variables influencing the risk of CD development and develop and validate clinical prediction models to provide individualized screening advice. METHODS: We analyzed prospective data from the 10 years of follow-up of the PreventCD-birth cohort involving 944 genetically predisposed children with CD-FDR. Variables significantly influencing the CD risk were combined to determine a risk score. Landmark analyses were performed at different ages. Prediction models were created using multivariable Cox proportional hazards regression analyses, backward elimination, and Harrell's c-index for discrimination. Validation was done using data from the independent NeoCel cohort. RESULTS: In March 2019, the median follow-up was 8.3 years (22 days-12.0 years); 135/944 children developed CD (mean age, 4.3 years [range, 1.1-11.4]). CD developed significantly more often in girls (P = .005) and in Human Leukocyte Antigen (HLA)-DQ2 homozygous individuals (8-year cumulative incidence rate of 35.4% vs maximum of the other HLA-risk groups 18.2% [P < .001]). The effect of homozygosity DR3-DQ2/DR7-DQ2 on CD development was only present in girls (interaction P = .04). The prediction models showed good fit in the validation cohort (Cox regression 0.81 [0.54]). To calculate a personalized risk of CD development and provide screening advice, we designed the Prediction application https://hputter.shinyapps.io/preventcd/. CONCLUSION: Children with CD-FDR develop CD early in life, and their risk depends on gender, age and HLA-DQ, which are all factors that are important for sound screening advice. These children should be screened early in life, including HLA-DQ2/8-typing, and if genetically predisposed to CD, they should get further personalized screening advice using our Prediction application. TRIAL REGISTRATION NUMBER: ISRCTN74582487 (https://www.isrctn.com/search?q=ISRCTN74582487).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.258
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations40
Published2022
Admission routes1
Has abstractyes

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